# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BERT finetuning runner."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
import codecs
import sys
from utils import selectGPU
import time
from multiprocessing.pool import Pool

import multiprocessing

import numpy
import pandas
from tqdm import tqdm

cur_dir = os.path.dirname(os.path.abspath(__file__))

import modeling2
import optimization
import tokenization
import tensorflow as tf
import random

flags = tf.flags
selectGPU()
FLAGS = flags.FLAGS

## Required parameters
flags.DEFINE_string(
    "sample_feature_file", None,
    "sample_feature_file to use")

flags.DEFINE_float(
    "pairloss_weight", 1.0,
    "margin for triplet loss")

flags.DEFINE_float(
    "pointloss_weight", 0.0,
    "margin for triplet loss")

flags.DEFINE_float(
    "margin", 0.5,
    "margin for triplet loss")

flags.DEFINE_string(
    "train_path", None,
    "The input train_path. Should contain the .tsv files (or other data files) "
    "for the task.")

flags.DEFINE_string(
    "dev_path", None,
    "The input dev_path. Should contain the .tsv files (or other data files) "
    "for the task.")

flags.DEFINE_string(
    "data_dir", None,
    "The input data dir. Should contain the .tsv files (or other data files) "
    "for the task.")

flags.DEFINE_string(
    "bert_config_file", None,
    "The config json file corresponding to the pre-trained BERT model. "
    "This specifies the model architecture.")

flags.DEFINE_string("task_name", None, "The name of the task to train.")

flags.DEFINE_string("vocab_file", None,
                    "The vocabulary file that the BERT model was trained on.")

flags.DEFINE_string(
    "output_dir", None,
    "The output directory where the model checkpoints will be written.")

## Other parameters

flags.DEFINE_string(
    "init_checkpoint", None,
    "Initial checkpoint (usually from a pre-trained BERT model).")

flags.DEFINE_bool(
    "do_lower_case", True,
    "Whether to lower case the input text. Should be True for uncased "
    "models and False for cased models.")

flags.DEFINE_integer(
    "max_seq_length", 128,
    "The maximum total input sequence length after WordPiece tokenization. "
    "Sequences longer than this will be truncated, and sequences shorter "
    "than this will be padded.")

flags.DEFINE_bool("do_train", True, "Whether to run training.")

flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")

flags.DEFINE_bool(
    "do_predict", False,
    "Whether to run the model in inference mode on the test set.")

flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")

flags.DEFINE_integer("eval_batch_size", 128, "Total batch size for eval.")

flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")

flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")

flags.DEFINE_float("num_train_epochs", 3.0,
                   "Total number of training epochs to perform.")

flags.DEFINE_float(
    "warmup_proportion", 0.1,
    "Proportion of training to perform linear learning rate warmup for. "
    "E.g., 0.1 = 10% of training.")

flags.DEFINE_integer("save_checkpoints_steps", 1000,
                     "How often to save the model checkpoint.")

flags.DEFINE_integer("iterations_per_loop", 1000,
                     "How many steps to make in each estimator call.")

flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")

tf.flags.DEFINE_string(
    "tpu_name", None,
    "The Cloud TPU to use for training. This should be either the name "
    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
    "url.")

tf.flags.DEFINE_string(
    "tpu_zone", None,
    "[Optional] GCE zone where the Cloud TPU is located in. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string(
    "gcp_project", None,
    "[Optional] Project name for the Cloud TPU-enabled project. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")

flags.DEFINE_integer(
    "num_tpu_cores", 8,
    "Only used if `use_tpu` is True. Total number of TPU cores to use.")


class InputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text_a, text_b=None, text_c=None):
        """Constructs a InputExample.

        Args:
          guid: Unique id for the example.
          text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
          text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
          label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.text_c = text_c


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, input_ids2, input_mask2, segment_ids2):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        # self.label_id = label_id

        self.input_ids2 = input_ids2
        self.input_mask2 = input_mask2
        self.segment_ids2 = segment_ids2


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_test_examples(self, data_dir):
        """Gets a collection of `InputExample`s for prediction."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with tf.gfile.Open(input_file, "r") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                lines.append(line)
            return lines


class rankProcessor(DataProcessor):
    """Processor for the shangfu data set."""

    def __init__(self):
        pass

    def _read_txt(self, txtpath):
        # print(txtpath)
        lines = []
        with codecs.open(txtpath, 'r', encoding='utf-8') as f:
            for line in f:
                # if len(lines) > 320:
                #     break
                try:
                    l = line.strip().split('\t')
                    lines.append(l)
                except:
                    continue
        return lines

    def get_train_examples(self, data_path):
        """See base class."""
        # lines = self._read_txt(data_path)
        # print('train_data num=', len(lines))

        examples = []
        data = pandas.read_csv(data_path, sep='\t', encoding='utf-8')
        # for (i, line) in enumerate(tqdm(lines)):
        for i, row in tqdm(data.iterrows(), total=data.shape[0]):
            # if i>5:
            #     break
            guid = "train-%d" % i
            text_a = tokenization.convert_to_unicode(row[0])
            text_b = tokenization.convert_to_unicode(row[1])
            text_c = tokenization.convert_to_unicode(row[2])
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=text_b, text_c=text_c))

        random.seed(2020)
        random.shuffle(examples)
        return examples

    # def get_labels(self):
    #     """See base class."""
    #     return ["1", "0"]


def convert_single_example(ex_index, example, label_list, max_seq_length,
                           tokenizer):
    """Converts a single `InputExample` into a single `InputFeatures`."""

    # label_map = {}
    # for (i, label) in enumerate(label_list):
    #     label_map[label] = i

    def pair_helper(text_1, text_2):

        tokens_a = tokenizer.tokenize(text_1)
        tokens_b = None
        if text_2:
            tokens_b = tokenizer.tokenize(text_2)

        if tokens_b:
            # Modifies `tokens_a` and `tokens_b` in place so that the total
            # length is less than the specified length.
            # Account for [CLS], [SEP], [SEP] with "- 3"
            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
        else:
            # Account for [CLS] and [SEP] with "- 2"
            if len(tokens_a) > max_seq_length - 2:
                tokens_a = tokens_a[0:(max_seq_length - 2)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids: 0     0   0   0  0     0 0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens = []
        segment_ids = []
        tokens.append("[CLS]")
        segment_ids.append(0)
        for token in tokens_a:
            tokens.append(token)
            segment_ids.append(0)
        tokens.append("[SEP]")
        segment_ids.append(0)

        if tokens_b:
            for token in tokens_b:
                tokens.append(token)
                segment_ids.append(1)
            tokens.append("[SEP]")
            segment_ids.append(1)

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        input_mask = [1] * len(input_ids)

        # Zero-pad up to the sequence length.
        while len(input_ids) < max_seq_length:
            input_ids.append(0)
            input_mask.append(0)
            segment_ids.append(0)

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length
        return input_ids, input_mask, segment_ids

    input_ids, input_mask, segment_ids = pair_helper(example.text_a, example.text_b)
    input_ids2, input_mask2, segment_ids2 = pair_helper(example.text_a, example.text_c)

    # label_id = label_map[example.label]
    # if ex_index < 5:
    #     tf.logging.info("*** Example ***")
    #     tf.logging.info("guid: %s" % (example.guid))
    #     tf.logging.info("tokens: %s" % " ".join(
    #         [tokenization.printable_text(x) for x in tokens]))
    #     tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
    #     tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
    #     tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
    #     tf.logging.info("label: %s (id = %d)" % (example.label, label_id))

    feature = InputFeatures(
        input_ids=input_ids,
        input_mask=input_mask,
        segment_ids=segment_ids,
        input_ids2=input_ids2,
        input_mask2=input_mask2,
        segment_ids2=segment_ids2)
    return feature


# def file_based_convert_examples_to_features(
#         examples, label_list, max_seq_length, tokenizer, output_file):
#     """Convert a set of `InputExample`s to a TFRecord file."""
#
#     writer = tf.python_io.TFRecordWriter(output_file)
#
#     for (ex_index, example) in enumerate(examples):
#         if ex_index % 10000 == 0:
#             tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
#
#         feature = convert_single_example(ex_index, example, label_list,
#                                          max_seq_length, tokenizer)
#
#         def create_int_feature(values):
#             f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
#             return f
#
#         features = collections.OrderedDict()
#         features["input_ids"] = create_int_feature(feature.input_ids)
#         features["input_mask"] = create_int_feature(feature.input_mask)
#         features["segment_ids"] = create_int_feature(feature.segment_ids)
#         features["input_ids2"] = create_int_feature(feature.input_ids2)
#         features["input_mask2"] = create_int_feature(feature.input_mask2)
#         features["segment_ids2"] = create_int_feature(feature.segment_ids2)
#         # features["label_ids"] = create_int_feature([feature.label_id])
#
#         tf_example = tf.train.Example(features=tf.train.Features(feature=features))
#         writer.write(tf_example.SerializeToString())

def file_based_convert_examples_to_features(
        examples, label_list, max_seq_length, tokenizer, output_file):
    """Convert a set of `InputExample`s to a TFRecord file."""

    writer = tf.python_io.TFRecordWriter(output_file)
    tf_example_str_list = multipool_convert_examples_to_feature(data=examples, f=convert_examples_to_feature_new,
                                                                chunk_size=5000, label_list=label_list,
                                                                max_seq_length=max_seq_length, tokenizer=tokenizer,
                                                                worker=-1)
    for tf_example_str in tf_example_str_list:
        writer.write(tf_example_str)


def convert_examples_to_feature_new(ex_indexs, examples, label_list, max_seq_length, tokenizer):
    tf_example_str_list = []

    for ex_index, example in tqdm(zip(ex_indexs, examples)):
        feature = convert_single_example(ex_index, example, label_list,
                                         max_seq_length, tokenizer)

        def create_int_feature(values):
            f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
            return f

        features = collections.OrderedDict()
        features["input_ids"] = create_int_feature(feature.input_ids)
        features["input_mask"] = create_int_feature(feature.input_mask)
        features["segment_ids"] = create_int_feature(feature.segment_ids)
        features["input_ids2"] = create_int_feature(feature.input_ids2)
        features["input_mask2"] = create_int_feature(feature.input_mask2)
        features["segment_ids2"] = create_int_feature(feature.segment_ids2)
        # features["label_ids"] = create_int_feature([feature.label_id])

        tf_example = tf.train.Example(features=tf.train.Features(feature=features))
        tf_example_str_list.append(tf_example.SerializeToString())

    return tf_example_str_list


# '多进程处理'
def multipool_convert_examples_to_feature(data, f, chunk_size, label_list, max_seq_length, tokenizer, worker=5):
    # cpu_worker = os.cpu_count()
    cpu_worker = multiprocessing.cpu_count()
    print('cpu 核心有：{}'.format(cpu_worker))
    if worker == -1 or worker > cpu_worker:
        worker = cpu_worker
    print('使用cpu:{}'.format(worker))
    t1 = time.time()
    len_data = len(data)
    start = 0
    end = 0
    p = Pool(worker)
    res = []  # 保存的每个进程的返回值
    while end < len_data:
        end = start + chunk_size
        if end > len_data:
            end = len_data
        rslt = p.apply_async(f, (range(start, end), data[start:end], label_list, max_seq_length, tokenizer,))
        start = end
        res.append(rslt)
    p.close()
    p.join()
    t2 = time.time()
    print((t2 - t1) / 60)
    # results = np.concatenate([i.get() for i in res], axis=0)
    results = []
    for i in res:
        results += i.get()
    return results


def file_based_input_fn_builder(input_file, seq_length, is_training,
                                drop_remainder):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""

    name_to_features = {
        "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
        "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),

        "input_ids2": tf.FixedLenFeature([seq_length], tf.int64),
        "input_mask2": tf.FixedLenFeature([seq_length], tf.int64),
        "segment_ids2": tf.FixedLenFeature([seq_length], tf.int64),
        # "label_ids": tf.FixedLenFeature([], tf.int64),
    }

    def _decode_record(record, name_to_features):
        """Decodes a record to a TensorFlow example."""
        example = tf.parse_single_example(record, name_to_features)

        # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
        # So cast all int64 to int32.
        for name in list(example.keys()):
            t = example[name]
            if t.dtype == tf.int64:
                t = tf.to_int32(t)
            example[name] = t

        return example

    def input_fn(params):
        """The actual input function."""
        batch_size = params["batch_size"]

        # For training, we want a lot of parallel reading and shuffling.
        # For eval, we want no shuffling and parallel reading doesn't matter.
        d = tf.data.TFRecordDataset(input_file)
        if is_training:
            d = d.repeat()
            d = d.shuffle(buffer_size=100)

        d = d.apply(
            tf.contrib.data.map_and_batch(
                lambda record: _decode_record(record, name_to_features),
                batch_size=batch_size,
                drop_remainder=drop_remainder))

        return d

    return input_fn


def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()


def create_model(bert_config, is_training, input_ids, input_mask, segment_ids, input_ids2, input_mask2, segment_ids2,
                 use_one_hot_embeddings):
    """Creates a classification model."""
    model = modeling2.BertModel(
        config=bert_config,
        is_training=is_training,
        input_ids=input_ids,
        input_mask=input_mask,
        token_type_ids=segment_ids,
        use_one_hot_embeddings=use_one_hot_embeddings)

    model2 = modeling2.BertModel(
        config=bert_config,
        is_training=is_training,
        input_ids=input_ids2,
        input_mask=input_mask2,
        token_type_ids=segment_ids2,
        use_one_hot_embeddings=use_one_hot_embeddings)

    # In the demo, we are doing a simple classification task on the entire
    # segment.
    #
    # If you want to use the token-level output, use model.get_sequence_output()
    # instead.
    output_layer = model.get_pooled_output()
    output_layer2 = model2.get_pooled_output()

    hidden_size = output_layer.shape[-1].value

    output_weights = tf.get_variable(
        "output_weights", [2, hidden_size],
        initializer=tf.truncated_normal_initializer(stddev=0.02))

    output_bias = tf.get_variable(
        "output_bias", [2], initializer=tf.zeros_initializer())

    # output_weights = tf.get_variable(
    #     "output_weights", [1, hidden_size],
    #     initializer=tf.truncated_normal_initializer(stddev=0.02))
    #
    # output_bias = tf.get_variable(
    #     "output_bias", [1], initializer=tf.zeros_initializer())

    # output_weights2 = tf.get_variable(
    #     "output_weights2", [1, hidden_size],
    #     initializer=tf.truncated_normal_initializer(stddev=0.02))
    #
    # output_bias2 = tf.get_variable(
    #     "output_bias2", [1], initializer=tf.zeros_initializer())

    with tf.variable_scope("loss"):
        if is_training:
            # I.e., 0.1 dropout
            output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
            output_layer2 = tf.nn.dropout(output_layer2, keep_prob=0.9)

        logits = tf.matmul(output_layer, output_weights, transpose_b=True)
        logits = tf.nn.bias_add(logits, output_bias)
        probabilities = tf.nn.softmax(logits, axis=-1)[:, 0]
        log_probs = tf.nn.log_softmax(logits, axis=-1)
        one_hot_labels = tf.one_hot([0] * log_probs.shape[0], depth=2, dtype=tf.float32)
        per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
        pos_loss = tf.reduce_mean(per_example_loss)

        logits2 = tf.matmul(output_layer2, output_weights, transpose_b=True)
        logits2 = tf.nn.bias_add(logits2, output_bias)
        probabilities2 = tf.nn.softmax(logits2, axis=-1)[:, 0]
        log_probs2 = tf.nn.log_softmax(logits2, axis=-1)
        one_hot_labels2 = tf.one_hot([1] * log_probs2.shape[0], depth=2, dtype=tf.float32)
        per_example_loss2 = -tf.reduce_sum(one_hot_labels2 * log_probs2, axis=-1)
        neg_loss = tf.reduce_mean(per_example_loss2)

        # pair_per_example_loss = tf.maximum(0.0, FLAGS.margin - probabilities + probabilities2)
        pair_per_example_loss = tf.maximum(0.0, FLAGS.margin - probabilities + probabilities2)
        pair_loss = tf.reduce_mean(pair_per_example_loss)

        loss = FLAGS.pointloss_weight * (pos_loss + neg_loss) + FLAGS.pairloss_weight * pair_loss

        return (loss, per_example_loss, logits, probabilities)


def model_fn_builder(bert_config, init_checkpoint, learning_rate,
                     num_train_steps, num_warmup_steps, use_tpu,
                     use_one_hot_embeddings):
    """Returns `model_fn` closure for TPUEstimator."""

    def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
        """The `model_fn` for TPUEstimator."""

        tf.logging.info("*** Features ***")
        for name in sorted(features.keys()):
            tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))

        input_ids = features["input_ids"]
        input_mask = features["input_mask"]
        segment_ids = features["segment_ids"]

        input_ids2 = features["input_ids2"]
        input_mask2 = features["input_mask2"]
        segment_ids2 = features["segment_ids2"]
        # label_ids = features["label_ids"]

        tf.logging.info("  labels = {}, mode = {}".format(labels, mode))

        is_training = (mode == tf.estimator.ModeKeys.TRAIN)

        (total_loss, per_example_loss, logits, probabilities) = create_model(
            bert_config, is_training, input_ids, input_mask, segment_ids, input_ids2, input_mask2, segment_ids2,
            use_one_hot_embeddings)

        tvars = tf.trainable_variables()
        initialized_variable_names = {}
        scaffold_fn = None
        if init_checkpoint:
            (assignment_map, initialized_variable_names
             ) = modeling2.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
            if use_tpu:

                def tpu_scaffold():
                    tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
                    return tf.train.Scaffold()

                scaffold_fn = tpu_scaffold
            else:
                tf.train.init_from_checkpoint(init_checkpoint, assignment_map)

        tf.logging.info("**** Trainable Variables ****")
        for var in tvars:
            init_string = ""
            if var.name in initialized_variable_names:
                init_string = ", *INIT_FROM_CKPT*"
            tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape,
                            init_string)

        output_spec = None
        if mode == tf.estimator.ModeKeys.TRAIN:

            train_op = optimization.create_optimizer(
                total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)

            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                loss=total_loss,
                train_op=train_op,
                scaffold_fn=scaffold_fn)
        elif mode == tf.estimator.ModeKeys.EVAL:

            def metric_fn(per_example_loss, logits):
                predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
                # accuracy = tf.metrics.accuracy(label_ids, predictions)
                loss = tf.metrics.mean(per_example_loss)
                return {
                    # "eval_accuracy": accuracy,
                    "eval_loss": loss,
                }

            eval_metrics = (metric_fn, [per_example_loss, logits])
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                loss=total_loss,
                eval_metrics=eval_metrics,
                scaffold_fn=scaffold_fn)
        else:
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode, predictions=probabilities, scaffold_fn=scaffold_fn)
        return output_spec

    return model_fn


# This function is not used by this file but is still used by the Colab and
# people who depend on it.
def input_fn_builder(features, seq_length, is_training, drop_remainder):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""

    all_input_ids = []
    all_input_mask = []
    all_segment_ids = []
    all_label_ids = []

    for feature in features:
        all_input_ids.append(feature.input_ids)
        all_input_mask.append(feature.input_mask)
        all_segment_ids.append(feature.segment_ids)
        all_label_ids.append(feature.label_id)

    def input_fn(params):
        """The actual input function."""
        batch_size = params["batch_size"]

        num_examples = len(features)

        # This is for demo purposes and does NOT scale to large data sets. We do
        # not use Dataset.from_generator() because that uses tf.py_func which is
        # not TPU compatible. The right way to load data is with TFRecordReader.
        d = tf.data.Dataset.from_tensor_slices({
            "input_ids":
                tf.constant(
                    all_input_ids, shape=[num_examples, seq_length],
                    dtype=tf.int32),
            "input_mask":
                tf.constant(
                    all_input_mask,
                    shape=[num_examples, seq_length],
                    dtype=tf.int32),
            "segment_ids":
                tf.constant(
                    all_segment_ids,
                    shape=[num_examples, seq_length],
                    dtype=tf.int32),
            "label_ids":
                tf.constant(all_label_ids, shape=[num_examples], dtype=tf.int32),
        })

        if is_training:
            d = d.repeat()
            d = d.shuffle(buffer_size=100)

        d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
        return d

    return input_fn


# This function is not used by this file but is still used by the Colab and
# people who depend on it.
def convert_examples_to_features(examples, label_list, max_seq_length,
                                 tokenizer):
    """Convert a set of `InputExample`s to a list of `InputFeatures`."""

    features = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))

        feature = convert_single_example(ex_index, example, label_list,
                                         max_seq_length, tokenizer)

        features.append(feature)
    return features


def main(_):
    tf.logging.set_verbosity(tf.logging.INFO)

    processors = {
        "rank": rankProcessor,
    }

    tf.logging.info("train:{},eval:{}".format(FLAGS.do_train, FLAGS.do_eval))

    if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
        raise ValueError(
            "At least one of `do_train`, `do_eval` or `do_predict' must be True.")

    bert_config = modeling2.BertConfig.from_json_file(FLAGS.bert_config_file)

    if FLAGS.max_seq_length > bert_config.max_position_embeddings:
        raise ValueError(
            "Cannot use sequence length %d because the BERT model "
            "was only trained up to sequence length %d" %
            (FLAGS.max_seq_length, bert_config.max_position_embeddings))

    tf.gfile.MakeDirs(FLAGS.output_dir)

    task_name = FLAGS.task_name.lower()

    if task_name not in processors:
        raise ValueError("Task not found: %s" % (task_name))

    processor = processors[task_name]()

    label_list = processor.get_labels()

    tokenizer = tokenization.FullTokenizer(
        vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)

    tpu_cluster_resolver = None
    if FLAGS.use_tpu and FLAGS.tpu_name:
        tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
            FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)

    is_per_host = False
    # is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
    run_config = tf.contrib.tpu.RunConfig(
        cluster=tpu_cluster_resolver,
        master=FLAGS.master,
        model_dir=FLAGS.output_dir,
        save_checkpoints_steps=FLAGS.save_checkpoints_steps,
        tpu_config=tf.contrib.tpu.TPUConfig(
            iterations_per_loop=FLAGS.iterations_per_loop,
            num_shards=FLAGS.num_tpu_cores,
            per_host_input_for_training=is_per_host))

    train_examples = None
    num_train_steps = None
    num_warmup_steps = None
    if FLAGS.do_train:
        train_examples = processor.get_train_examples(FLAGS.train_path)
        num_train_steps = int(
            len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
        num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)

    model_fn = model_fn_builder(
        bert_config=bert_config,
        # num_labels=len(label_list),
        init_checkpoint=FLAGS.init_checkpoint,
        learning_rate=FLAGS.learning_rate,
        num_train_steps=num_train_steps,
        num_warmup_steps=num_warmup_steps,
        use_tpu=FLAGS.use_tpu,
        use_one_hot_embeddings=FLAGS.use_tpu)

    # If TPU is not available, this will fall back to normal Estimator on CPU
    # or GPU.
    estimator = tf.contrib.tpu.TPUEstimator(
        use_tpu=FLAGS.use_tpu,
        model_fn=model_fn,
        config=run_config,
        train_batch_size=FLAGS.train_batch_size,
        eval_batch_size=FLAGS.eval_batch_size,
        predict_batch_size=FLAGS.predict_batch_size)

    if FLAGS.do_train:
        train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
        # 是否重新构造样本特征，很慢
        if FLAGS.sample_feature_file is None:
            file_based_convert_examples_to_features(
                train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file)
        else:
            train_file = FLAGS.sample_feature_file
            tf.logging.info('do not convert examples to feature, will use the old feature, feature file is %s',
                            train_file)

        tf.logging.info("***** Running training *****")
        tf.logging.info("  Num examples = %d", len(train_examples))
        tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
        tf.logging.info("  Num steps = %d", num_train_steps)
        train_input_fn = file_based_input_fn_builder(
            input_file=train_file,
            seq_length=FLAGS.max_seq_length,
            is_training=True,
            drop_remainder=True)

        estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)


def run():
    bert_base = '/Users/liu/data/bert/uncased_L-12_H-768_A-12'
    FLAGS.task_name = 'rank'
    FLAGS.do_train = True
    FLAGS.train_path = '/Users/liu/py2-workspace/wm20/src/model/bert/pairwise_data/train_pairwise.csv'
    FLAGS.vocab_file = os.path.join(bert_base, 'vocab.txt')
    FLAGS.bert_config_file = os.path.join(bert_base, 'bert_config.json')
    FLAGS.init_checkpoint = os.path.join(bert_base, 'bert_model.ckpt')
    # FLAGS.init_checkpoint = './temp'
    FLAGS.max_seq_length = 512
    FLAGS.train_batch_size = 4
    FLAGS.learning_rate = 5e-5
    FLAGS.num_train_epochs = 1.0

    FLAGS.margin = 0.4
    FLAGS.output_dir = './margin_{}'.format(FLAGS.margin)
    os.environ["CUDA_VISIBLE_DEVICES"] = '0'

    main('')


def run_online():
    bert_base = '../pretrain/uncased_L-12_H-768_A-12'
    FLAGS.task_name = 'rank'
    FLAGS.do_train = True
    FLAGS.vocab_file = os.path.join(bert_base, 'vocab.txt')
    FLAGS.bert_config_file = os.path.join(bert_base, 'bert_config.json')
    FLAGS.init_checkpoint = os.path.join(bert_base, 'bert_model.ckpt')
    # FLAGS.init_checkpoint = './temp'
    FLAGS.max_seq_length = 512
    FLAGS.train_batch_size = 16
    FLAGS.learning_rate = 5e-5
    FLAGS.num_train_epochs = 2.0
    FLAGS.train_path = './pairwise_data_/train_pairwise.csv'
    FLAGS.sample_feature_file = 'tf_record/train1V10.tf_record'
    FLAGS.margin = 0.4
    FLAGS.output_dir = './margin_{}_batch{}'.format(FLAGS.margin, FLAGS.train_batch_size)

    main('')


if __name__ == "__main__":
    # flags.mark_flag_as_required("data_dir")
    # flags.mark_flag_as_required("task_name")
    # flags.mark_flag_as_required("vocab_file")
    # flags.mark_flag_as_required("bert_config_file")
    # flags.mark_flag_as_required("output_dir")
    # tf.app.run()

    # run()
    run_online()
